College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Brief Bioinform. 2024 Jan 22;25(2). doi: 10.1093/bib/bbae113.
The breakthrough in cryo-electron microscopy (cryo-EM) technology has led to an increasing number of density maps of biological macromolecules. However, constructing accurate protein complex atomic structures from cryo-EM maps remains a challenge. In this study, we extend our previously developed DEMO-EM to present DEMO-EM2, an automated method for constructing protein complex models from cryo-EM maps through an iterative assembly procedure intertwining chain- and domain-level matching and fitting for predicted chain models. The method was carefully evaluated on 27 cryo-electron tomography (cryo-ET) maps and 16 single-particle EM maps, where DEMO-EM2 models achieved an average TM-score of 0.92, outperforming those of state-of-the-art methods. The results demonstrate an efficient method that enables the rapid and reliable solution of challenging cryo-EM structure modeling problems.
冷冻电镜（cryo-EM）技术的突破导致越来越多的生物大分子密度图的出现。然而，从 cryo-EM 图谱构建准确的蛋白质复合物原子结构仍然是一个挑战。在这项研究中，我们扩展了之前开发的 DEMO-EM 以呈现 DEMO-EM2，这是一种通过迭代组装过程将链和域级匹配和拟合相结合，从 cryo-EM 图谱构建蛋白质复合物模型的自动化方法，用于预测的链模型。该方法在 27 个冷冻电子断层扫描（cryo-ET）图谱和 16 个单颗粒 EM 图谱上进行了仔细评估，其中 DEMO-EM2 模型的平均 TM 评分达到 0.92，优于最先进的方法。结果表明该方法是一种高效的方法，能够快速可靠地解决具有挑战性的 cryo-EM 结构建模问题。
